A three-dimensional (3-D) image provides a perception of depth, and can be used in a variety of virtual environments. A virtual environment provides an interactive experience, in the form of virtual reality, to a user. 3-D imagery is becoming intensely popular in virtual environments that are experienced on screens, or special display devices such as head mounted devices or goggles. 3-D imagery is also used in gaming systems, simulations, architectural walkthroughs, and in several other scenarios.
The process of creating and displaying three-dimensional (3D) objects in an interactive computer environment is a complicated matter. The complexity increases with the need to convert a 2-D image to a corresponding 3-D model. A 2-D image includes two axes, whereas a 3-D image incorporates a third axis, which provides the depth component. It should be appreciated that the 3-D image is still being displayed on a two dimensional display but it has been modified, relative to the 2-D image, to include a dimensional depth that, when viewed by a user, makes the flat, planar image visually appear to be three dimensional.
Commonly available methods that convert a 2-D image to a corresponding 3-D model require combining multiple images that provide multiple views of the 2-D image. For example, a front view photo and a side view photo of a face may be required to recreate the face in 3-D. Some methods require specialized software programs to covert one or multiple 2-D input images to a 3-D output model. Yet other methods require a technician to work with specialized software programs to convert a 2-D image in to a corresponding 3-D model. These methods may significantly increase computational complexity, or may require time-consuming manual interventions in adjusting and/or aligning 2-D image(s) to create a corresponding 3-D model. Moreover, computerized methods of converting 2-D images of faces, such as faces of humans, pose several limitations in understanding the human face and features that vary widely with each individual. Some other methods, such as UV mapping, involve projecting a 2-D image on to a 3-D model surface to obtain texturized 2-D image. However, these methods are unable to match specific facial features from the 2-D image of a face to the corresponding 3-D mesh model.
There is a need for improved, automated methods and systems for converting a single 2-D image to a corresponding image with increased dimensional depth to create an image that appears 3-D. There is also a need for improved, automated methods and systems for converting a single 2-D image to a corresponding 3-D image in substantially real time, which can overcome the above limitations and disadvantages of the current methods.